'''simple docstring'''




import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
  import bitsandbytes as bnb

from copy import deepcopy


__UpperCAmelCase					=logging.getLogger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__ = None							,    UpperCamelCase__ = None							,    UpperCamelCase__ = None							,    UpperCamelCase__ = None							,    UpperCamelCase__ = None							,    UpperCamelCase__ = False							,    )      ->       Optional[int]:
    __lowerCamelCase            =    bnb_quantization_config.load_in_abit
    __lowerCamelCase            =    bnb_quantization_config.load_in_abit

    if load_in_abit and not is_abit_bnb_available():
        raise ImportError(
            '''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
            ''' make sure you have the latest version of `bitsandbytes` installed.'''							)
    if load_in_abit and not is_abit_bnb_available():
        raise ValueError(
            '''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
            '''make sure you have the latest version of `bitsandbytes` installed.'''							)

    __lowerCamelCase            =    []
    # custom device map
    if isinstance(UpperCamelCase__							,    UpperCamelCase__							) and len(device_map.keys()							) > 1:
        __lowerCamelCase            =    [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]

    # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
    if bnb_quantization_config.skip_modules is None:
        __lowerCamelCase            =    get_keys_to_not_convert(UpperCamelCase__							)

    # add cpu modules to skip modules only for 4-bit modules
    if load_in_abit:
        bnb_quantization_config.skip_modules.extend(UpperCamelCase__							)
    __lowerCamelCase            =    bnb_quantization_config.skip_modules

    # We add the modules we want to keep in full precision
    if bnb_quantization_config.keep_in_fpaa_modules is None:
        __lowerCamelCase            =    []
    __lowerCamelCase            =    bnb_quantization_config.keep_in_fpaa_modules
    modules_to_not_convert.extend(UpperCamelCase__							)

    # compatibility with peft
    __lowerCamelCase            =    load_in_abit
    __lowerCamelCase            =    load_in_abit

    __lowerCamelCase            =    get_parameter_device(UpperCamelCase__							)
    if model_device.type != "meta":
        # quantization of an already loaded model
        logger.warning(
            '''It is not recommended to quantize a loaded model. '''
            '''The model should be instantiated under the `init_empty_weights` context manager.'''							)
        __lowerCamelCase            =    replace_with_bnb_layers(UpperCamelCase__							,    UpperCamelCase__							,    modules_to_not_convert=UpperCamelCase__							)
        # convert param to the right dtype
        __lowerCamelCase            =    bnb_quantization_config.torch_dtype
        for name, param in model.state_dict().items():
            if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules							):
                param.to(torch.floataa							)
                if param.dtype != torch.floataa:
                    __lowerCamelCase            =    name.replace('''.weight'''							,    ''''''							).replace('''.bias'''							,    ''''''							)
                    __lowerCamelCase            =    getattr(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
                    if param is not None:
                        param.to(torch.floataa							)
            elif torch.is_floating_point(UpperCamelCase__							):
                param.to(UpperCamelCase__							)
        if model_device.type == "cuda":
            # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
            model.cuda(torch.cuda.current_device()							)
            torch.cuda.empty_cache()
        elif torch.cuda.is_available():
            model.to(torch.cuda.current_device()							)
        else:
            raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''							)
        logger.info(
            f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
            '''We move the model to cuda.'''							)
        return model

    elif weights_location is None:
        raise RuntimeError(
            f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """							)

    else:
        with init_empty_weights():
            __lowerCamelCase            =    replace_with_bnb_layers(
                UpperCamelCase__							,    UpperCamelCase__							,    modules_to_not_convert=UpperCamelCase__							)

        __lowerCamelCase            =    get_quantized_model_device_map(
            UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    max_memory=UpperCamelCase__							,    no_split_module_classes=UpperCamelCase__							,    )
        if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
            __lowerCamelCase            =    True

        __lowerCamelCase            =    any(x in list(device_map.values()							) for x in ['''cpu''', '''disk''']							)

        load_checkpoint_in_model(
            UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    dtype=bnb_quantization_config.torch_dtype							,    offload_folder=UpperCamelCase__							,    offload_state_dict=UpperCamelCase__							,    keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules							,    offload_abit_bnb=load_in_abit and offload							,    )
        return dispatch_model(UpperCamelCase__							,    device_map=UpperCamelCase__							,    offload_dir=UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							,    UpperCamelCase__=None							,    UpperCamelCase__=None							)      ->       Optional[Any]:
    if device_map is None:
        if torch.cuda.is_available():
            __lowerCamelCase            =    {'''''': torch.cuda.current_device()}
        else:
            raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''							)
        logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.'''							)

    if isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
            raise ValueError(
                '''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
                '''\'sequential\'.'''							)

        __lowerCamelCase            =    {}
        special_dtypes.update(
            {
                name: bnb_quantization_config.torch_dtype
                for name, _ in model.named_parameters()
                if any(m in name for m in bnb_quantization_config.skip_modules							)
            }							)
        special_dtypes.update(
            {
                name: torch.floataa
                for name, _ in model.named_parameters()
                if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules							)
            }							)

        __lowerCamelCase            =    {}
        __lowerCamelCase            =    special_dtypes
        __lowerCamelCase            =    no_split_module_classes
        __lowerCamelCase            =    bnb_quantization_config.target_dtype

        # get max_memory for each device.
        if device_map != "sequential":
            __lowerCamelCase            =    get_balanced_memory(
                UpperCamelCase__							,    low_zero=(device_map == '''balanced_low_0''')							,    max_memory=UpperCamelCase__							,    **UpperCamelCase__							,    )

        __lowerCamelCase            =    max_memory
        __lowerCamelCase            =    infer_auto_device_map(UpperCamelCase__							,    **UpperCamelCase__							)

    if isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        # check if don't have any quantized module on the cpu
        __lowerCamelCase            =    bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

        __lowerCamelCase            =    {
            key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
        }
        for device in ["cpu", "disk"]:
            if device in device_map_without_some_modules.values():
                if bnb_quantization_config.load_in_abit:
                    raise ValueError(
                        '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''							)
                else:
                    logger.info(
                        '''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'''							)
        del device_map_without_some_modules
    return device_map






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							,    UpperCamelCase__=None							)      ->       Dict:

    if modules_to_not_convert is None:
        __lowerCamelCase            =    []

    __lowerCamelCase      ,    __lowerCamelCase            =    _replace_with_bnb_layers(
        UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    if not has_been_replaced:
        logger.warning(
            '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
            ''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
            ''' Please double check your model architecture, or submit an issue on github if you think this is'''
            ''' a bug.'''							)
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=None							,    UpperCamelCase__=None							,    )      ->       Any:
    __lowerCamelCase            =    False
    for name, module in model.named_children():
        if current_key_name is None:
            __lowerCamelCase            =    []
        current_key_name.append(UpperCamelCase__							)
        if isinstance(UpperCamelCase__							,    nn.Linear							) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            __lowerCamelCase            =    '''.'''.join(UpperCamelCase__							)
            __lowerCamelCase            =    True
            for key in modules_to_not_convert:
                if (
                    (key in current_key_name_str) and (key + "." in current_key_name_str)
                ) or key == current_key_name_str:
                    __lowerCamelCase            =    False
                    break
            if proceed:
                # Load bnb module with empty weight and replace ``nn.Linear` module
                if bnb_quantization_config.load_in_abit:
                    __lowerCamelCase            =    bnb.nn.LinearabitLt(
                        module.in_features							,    module.out_features							,    module.bias is not None							,    has_fpaa_weights=UpperCamelCase__							,    threshold=bnb_quantization_config.llm_inta_threshold							,    )
                elif bnb_quantization_config.load_in_abit:
                    __lowerCamelCase            =    bnb.nn.Linearabit(
                        module.in_features							,    module.out_features							,    module.bias is not None							,    bnb_quantization_config.bnb_abit_compute_dtype							,    compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant							,    quant_type=bnb_quantization_config.bnb_abit_quant_type							,    )
                else:
                    raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False'''							)
                __lowerCamelCase            =    module.weight.data
                if module.bias is not None:
                    __lowerCamelCase            =    module.bias.data
                bnb_module.requires_grad_(UpperCamelCase__							)
                setattr(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
                __lowerCamelCase            =    True
        if len(list(module.children()							)							) > 0:
            __lowerCamelCase      ,    __lowerCamelCase            =    _replace_with_bnb_layers(
                UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
            __lowerCamelCase            =    has_been_replaced | _has_been_replaced
        # Remove the last key for recursion
        current_key_name.pop(-1							)
    return model, has_been_replaced






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:
    # Create a copy of the model
    with init_empty_weights():
        __lowerCamelCase            =    deepcopy(UpperCamelCase__							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

    __lowerCamelCase            =    find_tied_parameters(UpperCamelCase__							)
    # For compatibility with Accelerate < 0.18
    if isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        __lowerCamelCase            =    sum(list(tied_params.values()							)							,    []							) + list(tied_params.keys()							)
    else:
        __lowerCamelCase            =    sum(UpperCamelCase__							,    []							)
    __lowerCamelCase            =    len(UpperCamelCase__							) > 0

    # Check if it is a base model
    __lowerCamelCase            =    False
    if hasattr(UpperCamelCase__							,    '''base_model_prefix'''							):
        __lowerCamelCase            =    not hasattr(UpperCamelCase__							,    model.base_model_prefix							)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    __lowerCamelCase            =    list(model.named_children()							)
    __lowerCamelCase            =    [list_modules[-1][0]]

    # add last module together with tied weights
    __lowerCamelCase            =    set(UpperCamelCase__							) - set(UpperCamelCase__							)
    __lowerCamelCase            =    list(set(UpperCamelCase__							)							) + list(UpperCamelCase__							)

    # remove ".weight" from the keys
    __lowerCamelCase            =    ['''.weight''', '''.bias''']
    __lowerCamelCase            =    []
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                __lowerCamelCase            =    name.replace(UpperCamelCase__							,    ''''''							)
        filtered_module_names.append(UpperCamelCase__							)

    return filtered_module_names






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:
    for m in model.modules():
        if isinstance(UpperCamelCase__							,    bnb.nn.Linearabit							):
            return True
    return False






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[int]:
    return next(parameter.parameters()							).device






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
    if fpaa_statistics is None:
        set_module_tensor_to_device(UpperCamelCase__							,    UpperCamelCase__							,    0							,    dtype=UpperCamelCase__							,    value=UpperCamelCase__							)
        __lowerCamelCase            =    param_name
        __lowerCamelCase            =    model
        if "." in tensor_name:
            __lowerCamelCase            =    tensor_name.split('''.'''							)
            for split in splits[:-1]:
                __lowerCamelCase            =    getattr(UpperCamelCase__							,    UpperCamelCase__							)
                if new_module is None:
                    raise ValueError(f"""{module} has no attribute {split}."""							)
                __lowerCamelCase            =    new_module
            __lowerCamelCase            =    splits[-1]
        # offload weights
        __lowerCamelCase            =    False
        offload_weight(module._parameters[tensor_name]							,    UpperCamelCase__							,    UpperCamelCase__							,    index=UpperCamelCase__							)
        if hasattr(module._parameters[tensor_name]							,    '''SCB'''							):
            offload_weight(
                module._parameters[tensor_name].SCB							,    param_name.replace('''weight'''							,    '''SCB'''							)							,    UpperCamelCase__							,    index=UpperCamelCase__							,    )
    else:
        offload_weight(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    index=UpperCamelCase__							)
        offload_weight(UpperCamelCase__							,    param_name.replace('''weight'''							,    '''SCB'''							)							,    UpperCamelCase__							,    index=UpperCamelCase__							)

    set_module_tensor_to_device(UpperCamelCase__							,    UpperCamelCase__							,    '''meta'''							,    dtype=UpperCamelCase__							,    value=torch.empty(*param.size()							)							)

'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="levit"






     def __init__(					self						:						Optional[int]    ,							a						:						int=2_24    ,							a						:						Union[str, Any]=3    ,							a						:						List[Any]=3    ,							a						:						Dict=2    ,							a						:						Tuple=1    ,							a						:						Optional[int]=16    ,							a						:						Optional[Any]=[1_28, 2_56, 3_84]    ,							a						:						Dict=[4, 8, 12]    ,							a						:						Optional[Any]=[4, 4, 4]    ,							a						:						Optional[int]=[16, 16, 16]    ,							a						:						Any=0    ,							a						:						List[Any]=[2, 2, 2]    ,							a						:						Optional[Any]=[2, 2, 2]    ,							a						:						Union[str, Any]=0.02    ,							**a						:						Optional[int]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    kernel_size
         __lowerCamelCase            =    stride
         __lowerCamelCase            =    padding
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    num_attention_heads
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    key_dim
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    attention_ratio
         __lowerCamelCase            =    mlp_ratio
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    [
             ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Tuple				=version.parse("1.11"					)






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         return 1e-4
